Optimization of Injection Molding Process Parameters with Material Properties Based on GA and BP

Tan, Xiao Hong; Wang, Lei Gang; Wang, Wen Shen

doi:10.4028/www.scientific.net/amm.345.586

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…The combination of BPNN and GA benefit the BPNN by searching the best fit value while BPNN works in learning to predict the accuracy in finding the optimum combination parameters. This study uses packing time, melt temperature, packing pressure and mould temperature as their parameter settings [39].…”

Section: A Comparison From the Previous Studymentioning

confidence: 99%

Overview of the Injection Moulding Optimisation for Shrinkage Defect

et al. 2018

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The injection moulding process in manufacturing plastic material has become a worldwide used for more than 100 years. The used can be seen everywhere instead of became a daily used product. The complexity of the process makes it difficult to handle but the big production makes it possible to happen. The entire process not just involve the difficult cycling process, but the involvement of the materials which pertaining many types of characteristic which considered to. Every big production need a quality production product with suitable cost and time efficiency. Eventhough, alongside the process, many defects can be detected by the physically seen marks. Many people especially from the expert’s field are trying to minimise as much as they can to reduce the defects so that the residual cost can be minimise. One of the way to overcome the problems is by simulation before the real product is being produce. The simulation is made by two classes of types which classical and non-classical method. Either both of the method gave good result but it still not reaches a perfect solution. In this study, the optimisation method of non-classical and classical are used to minimise the shrinkage defects at the thick plate part.

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Section: A Comparison From the Previous Studymentioning

confidence: 99%

Overview of the Injection Moulding Optimisation for Shrinkage Defect

et al. 2018

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AI approaches: Recent studies on shrinkage optimisation in injection moulding process

Hatta

Zain

Shayfull

et al. 2018

AIP Conference Proceedings

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Predicting and Controlling the Quality of Injection Molding Properties for Fiber-Reinforced Composites

Wang¹,

Fan²,

Guo³

et al. 2023

SAE Int. J. Mater. Manf.

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<div>Fiber-reinforced composites are widely used in injection molding processes because of their high strength and high elastic modulus. However, the addition of reinforcing agents such as glass fibers has a significant impact on their injection molding quality. The difference in shrinkage and hardness between the plastic and the reinforcement will bring about warpage and deformation in the injection molding of the product. At the same time, the glass fibers will be oriented in the flow direction during the injection molding process. This will enhance the mechanical properties in the flow direction and increase the shrinkage in the vertical direction, reducing the molding quality of the product. In this study, a test program was developed based on the Box-Behnken test design in the Design-Expert software, using a plastic part as an example. Moldflow software was used for simulation, and data analysis of the experimental data was carried out to investigate the significance of the influence of each injection molding process parameter on the molding quality. In addition to this, a mathematical model between the injection molding process parameters and the quality objectives was established by optimizing the model parameters of the back-propagation (BP) neural network through the ant colony optimization (ACO) algorithm. The established mathematical model is then globally optimized using a multi-objective function optimization based on the non-dominated rank-based sorting genetic algorithm (NSGA-II) to obtain the optimal combination of process parameters. The research in this article provides a theoretical basis for further combining intelligent algorithms to improve injection molding quality.</div>

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Optimization of Injection Molding Process Parameters with Material Properties Based on GA and BP

Cited by 3 publications

References 3 publications

Overview of the Injection Moulding Optimisation for Shrinkage Defect

Overview of the Injection Moulding Optimisation for Shrinkage Defect

AI approaches: Recent studies on shrinkage optimisation in injection moulding process

Predicting and Controlling the Quality of Injection Molding Properties for Fiber-Reinforced Composites

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